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1 INTRODUCTION

The development and application of most present- day systems and control theory were spurred on by the need to resolve aerospace problems. This is roughly the problem of analyzing and designing guidance law and flight control systems (autopilot) for tactical missiles or aircraft.

The guidance and control laws used in current tactical missiles are mainly based on classical control design techniques. These control laws were developed in the 1950s and have evolved into fairly standard design procedures Earlier guidance techniques worked well for targets that were large and traveled at lower speeds. However, these techniques are no longer effective against the new generation targets that are small, fast, and highly maneuverable.

In addition, the missile-target dynamics are highly nonlinear and prior approximations or simplifications have generally been required before the analytical guidance gains can be derived in the traditional approaches .Therefore, one does not know exactly what the true missile model is, and the missile behavior may change in unpredictable ways. Consequently, one cannot ensure optimality of the resulting design.

In the last three decades, optimality-based guidance designs have been considered to be the most effective way for a guided missile engaging the target but it is too complex for real-time onboard implementation.

Based on the reasons given above, advanced control theory must be applied to a missile guidance and control system to improve its performance. The use of intelligent control systems has infiltrated the modern world. Specific features of intelligent control include 1decision making

2 adaptation to uncertain media

3 self-organization

4 planning and scheduling operations

Very often, no preferred mathematical model is presumed in the problem formulation, and information is presented in a descriptive manner. Therefore, it may be the most effective way to solve the above problems.

Intelligent control is a control technology that replaces the human mind in making decisions, planning control strategies, and learning new functions whenever the environment does not allow or does not justify the presence of a human operator. Artificial neural networks and fuzzy logic are two potential tools for use in applications in intelligent control engineering.

Artificial neural networks offer the advantage of performance improvement through learning by means of parallel and distributed processing.

. The use of fuzzy logic control is motivated by the need to deal with highly nonlinear flight control and performance robustness problems. It is well known that fuzzy logic is much closer to human decision making than traditional logical systems. Fuzzy control based on fuzzy logic provides a new design paradigm such that a controller can be designed for complex, illdefined processes without knowledge of quantitative data regarding the input-output relations, which are otherwise required by conventional approaches . [1]

2 CONVENTIONAL GUIDANCE AND CONTROL DESIGN

Tactical missiles are normally guided from shortly after launch until target interception. The guidance and control system supplies steering commands to aerodynamic control surfaces or to correct elements of the thrust vector subsystem so as to point the missile towards its target and make it possible for the weapon to intercept a maneuvering target.

A basic homing loop for missile-target engagement is illustrated in Fig. 1.

2.1 GUIDANCE

From the viewpoint of a control configuration, guidance is a special type of compensation network that is placed in series with a flight control system (also called autopilot) to accomplish an intercept. Its purpose is to determine appropriate pursuer flight path dynamics such that some pursuer objective can be achieved efficiently. For most effective counterattack strategies, different guidance laws may need to be used to accomplish the mission for the entire trajectory.

First, midcourse guidance refers to the process of guiding a missile that cannot detect its target when launched; it is primarily an energy management and inertial instrumentation problem. When a radar seeker is locked onto a target and is providing reliable tracking data, such as the missile-target relative range, line-of-sight (LOS) angle, LOS angle rate

and boresight error angle, the guidance strategy in this phase is called terminal guidance. Steering of the missile during this period of flight has the most direct effect on the final miss distance. The steering law should be capable of achieving successful intercept in the presence of target maneuvers and external and internal disturbances.

2.2 FLIGHT CONTROL SYSTEM

The flight control system executes commands issued based on the guidance law with fidelity during flight. Its function is three-fold: it provides the required missile lateral acceleration characteristics, it stabilizes or damps the bare airframe, and it reduces the missile performance sensitivity to disturbance inputs over the required flight envelope.

2.3 CONVENTIONAL DESIGN METHODS

many different guidance laws have been exploited based on various design concepts over the years. Currently, the most popular terminal guidance laws defined by Locke involve LOS guidance, LOS rate guidance, command-to-line-ofsight (CLOS) guidance and other advanced guidance strategies. Among the current techniques, guidance commands proportional to the LOS angle rate are generally used by most high-speed missiles today to correct the missile course in the guidance loop. This approach is referred to as PNG and is quite successful against nonmaneuvering targets. While PNG exhibits optimal performance with a constant-velocity target, it is not effective in the presence of target maneuvers and often leads to unacceptable miss distances.

Among the midcourse guidance laws, the most effective and simplest one is the explicit guidance law .

The flight control system used in almost all operational homing missiles today is a three loop autopilot, composed of a rate loop, an accelerometer, and a synthetic stability loop.

Current highly maneuverable fighters pose a challenge to contemporary missiles employing classical guidance techniques to intercept these targets. Guidance laws currently in use on existing and fielded missiles may be inadequate in battlefield environments. Performance criteria will probably require application of newly developed theories, which in turn will necessitate a large computation capability compared to the classical guidance strategy. However, advances in microprocessors and digital signal processors allow increased use of onboard computers to perform more sophisticated computation using guidance and control algorithms. [1]

3 NEURAL NET-BASED GUIDANCE AND CONTROL DESIGN

The application of neural networks has attracted significant attention in several disciplines, such as signal processing, identification and control. The success of neural networks is mainly attributed to their unique features:

(1) Parallel structures with distributed storage and processing of massive amounts of information.

(2) Learning ability made possible by adjusting the network interconnection weights and biases based on certain learning algorithms.

The first feature enables neural networks to process large amounts of dimensional information in real-time (e.g. matrix computations), hundreds of times faster than the numerically serial computation performed by a computer. The implication of the second feature is that the nonlinear dynamics of a system can be learned and identified directly by an artificial neural network. The network can also adapt to changes in the environment and make decisions despite uncertainty in operating conditions.

The first application of neural networks to control systems was developed in the mid-1980s. Models of dynamic systems and their inverses have immediate utility in control. Some of the well-established and well-analyzed structures which have been applied in guidance and control designs are described below.

3.1 SUPERVISORY CONTROL

The neural controller in the system is utilized as an inverse system model. The inverse model is simply cascaded with the controlled system such that the system produces an identity mapping between the desired response and controlled system output.

The control scheme is very common in robotics application and autopilot design.

3.2 HYBRID CONTROL

. It was thought that off-line learning of a rough approximation to the desired control law should be performed first, which is called generalized learning. Then, the neural control will be capable of driving the plant over the operating range and without instability. A period of on-line specialized learning can then be used to improve the control provided by the neural network controller. An alternative is to utilize a linear, fixed gain controller in parallel with the neural control law. This fixed gain control law is first chosen to stabilize the plant. The plant is then driven over the operating range with the neural network tuned online to improve the control

3.3 MODEL REFERENCE CONTROL

The two control schemes presented above do not consider the tracking performance. In this scheme, the desired performance of the closed-loop system is specified through a stable reference model. The control system attempts to make the plant output match the reference model output asymptotically. In this scheme, the error between the plant and the reference model outputs is used to adjust the weights of the neural controller.

3.4 INTERNAL MODEL CONTROL (IMC)

In this scheme, the network NN1 is first trained off-line to emulate the controlled plant dynamics directly. During on-line operation, the error between the model and the measured plant output is used as a feedback signal and passed to the neuro controller NN2. The IMC has been thoroughly examined and shown to yield stability robustness. This approach can be extended readily to autopilot designs for nonlinear airframes under external disturbances.

3.5 ADAPTIVE LINEAR OR NONLINEAR CONTROL

In this tracking error cost is evaluated according to some performance index. The result is then used as a basis for adjusting the connection weights of the neural network. The weights are adjusted on-line using basic backpropagation rather than off-line. The control design method exploits the advantages of both neutral networks and robust adaptive control theory.

3.6. PREDICTIVE CONTROL In this neural network provides prediction of future plant response over a specified horizon. The predictions supplied by the network are then passed on to a numerical optimization routine, which attempts to minimize specified performance criteria in the calculation of a suitable control signal.

3.7. REINFORCEMENT LEARNING CONTROL

This control scheme is a minimally supervised learning algorithm; the only information that is made available is whether or not a particular set of control actions has been successful. Instead of trying to determine target controller outputs from target plant responses, one tries to determine a target controller output that will lead to an improvement in plant performance. The critic block is capable of evaluating the plant performance and generating an evaluation signal, which can be, used the reinforcement learning algorithm. This approach is appropriate when there is a genuine lack of knowledge required to apply more specialized learning methods.

4 FUZZY LOGIC-BASED GUIDANCE AND CONTROL DESIGN

The existing applications of fuzzy control range from micro-controller based systems in home applications to advanced flight control systems. The main advantages of using fuzzy are as follows:

(1) It is implemented based on human operatorâ„¢s expertise which does not lend itself to being easily expressed in conventional proportional integral- derivative parameters of differential equations, but rather in situation/action rules.

(2) For an ill-conditioned or complex plant model, fuzzy control offers ways to implement simple but robust solutions that cover a wide range of system parameters and, to some extent, can cope with major disturbances.

The sequence of operations in a fuzzy system can be described in three phases called fuzzification, inference, and defuzzification shown as in Fig11. A fuzzification interface converts input data into suitable linguistic values that may be viewed as labels of fuzzy sets. An inference mechanism can infer fuzzy control actions employing fuzzy implication and the rules of the interface in fuzzy logic. A defuzzification interface yields a nonfuzzy control action from an inferred fuzzy control action. The knowledge base involves the control policy for the human expertise and necessary information for the proper functioning of the fuzzification and defuzzification modules.

Fuzzy control was first introduced and applied in the 1970â„¢s in an attempt to design controllers for systems that were structurally difficult to model. It is now being used in a large number of domains. Fuzzy algorithms can be found in various fields, such as estimation, decision making and, especially, automatic control. [2]

4.1. FUZZY PROPORTIONAL-INTEGRAL-DERIVATIVE (PID) CONTROL

In this case, fuzzy rules and reasoning are utilized on-line to determine the control action based on the error signal and its first derivative or difference. The conventional fuzzy two-term control has two different types: one is fuzzy-proportional-derivative (fuzzy-PD) control, which generates a control output from the error and change rate of error, and is a position type control; the other is the fuzzy-proportional- integral (fuzzy-PI) control, which generates an incremental control output from the error and change rate of error, and is a velocity type control it has been shown that these fuzzy guidance systems perform better than traditional navigational or augmented proportional navigation schemes that is smaller miss distance and less acceleration command.

4.2 HYBRID FUZZY CONTROLLER

Fuzzy controllers can have inputs generated by a conventional controller. Typically, the error is first input to a conventional controller. The conventional controller filters this signal. The filtered error is then input to the fuzzy system. This constitutes a hybrid fuzzy control scheme. Since the error signal is purified, one needs fewer fuzzy sets describing the domain of the error signal. Based on this specific feature, these types of controllers are robust and need a less complicated rule base.

4.3 FUZZY ADAPTIVE CONTROLLER

The structure is similar to that of fuzzy PID controllers. However, the shapes of the input/output membership functions are adjustable and can adapt to instantaneous error. Since the membership functions are adaptable, the controller is more robust and more insensitive to plant parameter variations. Adaptive fuzzy autopilot is developed for bank to turn missiles and an adaptive fuzzy system was applied to autopilot design of the X-29 fighter.

4.5. FUZZY MODEL-FOLLOWING CONTROLLER

To have the advantages of a fuzzy logic controller with a desired level of performance, a fuzzy adaptive controller can be used in a model-following control system .In this scheme, the error between the plant output and the reference model output is used to adjust the membership functions of the fuzzy controller .

4.6 HIERARCHICAL FUZZY CONTROLLER

In a hierarchical fuzzy controller the structure is divided into different levels. The hierarchical controller gives an approximate output at the first level, which is then modified by the second level rule set. This process is repeated in succeeding hierarchical.

4.7. OPTIMAL CONTROL

In this approach, exact open-loop optimal control data from the computed optimal time histories of state and control variables are used to generate fuzzy rules for fuzzy logic guidance. First, data related to the state and control variables of optimal guidance are generated using several scenarios of interest. The collected data are then used to train the networkâ„¢s weights. After training has been performed successfully missile trajectories and acceleration commands for the optimal solution and fuzzy logic guidance solution will be close during actual flight using these scenarios.

5 GAIN-SCHEDULING GUIDANCE AND CONTROL DESIGN

Gain-scheduling is an old control engineering technique which uses process variables related to dynamics to compensate for the effect caused by working in different operating regions. It is an effective way to control systems whose dynamics change with the operating conditions. It is normally used in the control of nonlinear plants in which the relationship between the plant dynamics and operating conditions is known, and for which a single linear timeinvariant model is insufficient. This specific feature makes it especially suitable for guidance and control design problems.

Gain-scheduling design involves three main tasks: partitioning of the operating region into several approximately linear regions, designing a local controller for each linear region, and interpolation of controller parameters between the linear regions. The main advantage of gain-scheduling is that controller parameters can be adjusted very quickly in response to changes in the plant dynamics. It is also simpler to implement than automatic tuning or adaptation. [3]

5.1. CONVENTIONAL GAIN-SCHEDULING (CGS)

In this the controller parameters are changed in an open-loop fashion based on measurements of the operating conditions of the plant. A gain-scheduled control system can, thus, be viewed as a feedback control system in which the feedback gains are adjusted using feed forward compensation.

5.2. FUZZY GAIN-SCHEDULING (FGS)

The main drawback of CGS is that the parameter change may be rather abrupt across the boundaries of the region, which may result in unacceptable or even unstable performance. Another problem is that accurate linear time-invariant models at various operating points may be difficult, if not impossible, to obtain. As a solution to these problems, FGS has been proposed, which utilizes a fuzzy reasoning technique to determine the controller parameters . For this approach, human expertise in the linear control design and CGS are represented by means of fuzzy rules, and a fuzzy inference mechanism is used to interpolate the controller parameters in the transition regions

The FGS technique has been used in missile guidance and flight control design.

5.3. NEURAL NETWORK GAIN-SCHEDULING (NNGS)

NNGS can incorporate the learning ability into gain-scheduling control. The training example consists of operating variables and control gains obtained at various operating points and their corresponding desired outputs. The main advantage of NNGS is that it avoids the need to manually design a scheduling program or determine a suitable inferencing system.

The neural gain-scheduling technique has been used in various fields, such as hydroelectric generation process control , robotic manipulators and aircraft flight control systems.

5.4. NEURAL-FUZZY GAIN-SCHEDULING (NFGS)

NFGS is implemented using a neural-fuzzy network that seeks to integrate the representational power of a fuzzy inferencing system and the learning and function approximation abilities of a neural network to produce a gain scheduling system .In contrast to NNGS, NFGS provides a more meaningful interpretation of the network; in addition, expert knowledge can be incorporated into the fuzzy rules and membership functions.

6 ADVANTAGES AND DISADVANTAGES

6.1 ADVANTAGES

(1) Fuzzy guidance and control provides a new design paradigm such that a control mechanism based on expertise can be designed for complex, ill-defined flight dynamics without knowledge of quantitative data regarding the input-output relations, which are required by conventional approaches. A fuzzy logic control scheme can produce a higher degree of automation and offers ways to implement simple but robust solutions that cover a wide range of aerodynamic parameters and can cope with major external disturbances.

(2) Artificial Neural networks constitute a promising new generation of information processing systems that demonstrate the ability to learn, recall, and generalize from training patterns or data. This specific feature offers the advantage of performance improvement for illdefined flight dynamics through learning by means of parallel and distributed processing. Rapid adaptation to environment change makes them appropriate for guidance and control systems because they can cope with aerodynamic changes during flight.

6.2 GENERAL DRAWBACKS

(1) Performance of intelligent control systems during the transient stage is usually not reliable. This problem should be avoided in guidance and control systems. A hybrid control scheme, which combines an intelligent controller with a conventional controller, is better. In fact, in most cases, there are no pure neural or fuzzy solutions, but rather hybrid solutions when intelligent control is used to augment conventional control.

(2) The lack of satisfactory formal techniques for studying the stability of intelligent control systems is a major drawback.

(3) Only if there is relevant knowledge about the plant and its control variables expressible in terms of neural networks or fuzzy logic can this advanced control technology lead to a higher degree of automation for complex, illstructured airframes.

(4) Besides reports and experimental work necessary to develop these methods, we need a much broader basis of experience with successful or unsuccessful applications.

7 CONCLUSIONS

It has been the general focus of this report to summarize the basic knowledge about intelligent control structures for the development of guidance and control systems. The great potential of intelligent control in guidance and control has recently been realized. Neural network based controllers with parallel processing and distributed storage have learning ability .It is this ability of neural network that make it a potential tool for guidance systems. Fuzzy logic controllers which make decisions like a human expertise will definitely be common in guidance and control systems. For completeness, conventional, neural net-based, fuzzy logic-based, gain-scheduling, and adaptive guidance and control techniques have been briefly summarized. It is clear that rapid advances in the development of microprocessors and DSP â„¢s along with tools like fuzzy logic and neural network will revolutionize the field of guidance and control.

8 REFERENCES

1) Chun-Liang Lin And Huai-Wen Su. , Intelligent Control Theory in Guidance and

Control System Design: an Overview â„¢â„¢Proc. Natl. Sci, Counc. ROC(A) ,Vol. 24, No. 1,

2000. pp. 15-30

2) Boulet, V., E. Druon, D. Willaeys, and P. Vanheeghe (1993) Target estimation using fuzzy logic. Proc. 1993 IEEE Int. Conf. Syst., Man and Cyb., Piscataway, NJ, U.S.A.

3) Carter, L. H. and J. S. Shamma (1996) Gain-scheduled bank-toturn autopilot design

Using linear parameter varying transformations. J. Guid., Contr. and Dyna., 19(5),

1056-1063 .